Last week

• Tall versus wide data frames (tibbles)
  • Why? iterate row-by-row without FOR Loops
    • Example: ggplot2::facet_wrap()
  • How? tidyr::pivot_longer() | tidyr::pivot_wider()

Summary Review

R can feel like a different galaxy

What is R

• A data-first, functional, programming language

• Functional programming languages mean you don’t have to write FOR loops

• Rule of Thumb: If you compose an expression three or more times, write a function

Everything in R

Every object is either a vector or a function

my_vector <- 7:14   # a numeric vector
dplyr::select()     # A function from the dplyr package

Vectors are data

All data are vectors

Data types

• Vectors

• Data Frames (i.e. Tibbles) are 2 dimensional vectors

• Lists

• Matrices

Many Tidyverse functions are iterable

Many tidyverse functions are “vectorized” and can be invoked with an implied FOR loops

library(tidyverse)

starwars |> 
  mutate(name_lc = str_to_lower(name), .after = name)

What is a FOR Loop

A flow control-flow-statement

for (variable in sequence) {
    expression
}

Custom Functions

add_numbers <- function(x, y) {
  x + y
}

starwars |> 
  select(where(is.numeric)) |> 
# mutate(my_sum = height + mass)  
  mutate(my_sum = add_numbers(height, mass)) 

♠

Environment variables and data variabls

make_scatterplot_with_vars <- function(my_df, my_x, my_y) {
  my_df |> 
    ggplot(aes({{my_x}}, {{my_y}})) + 
    geom_point()
}

starwars |> 
  filter(mass < 500) |> 
  make_scatterplot_with_vars(my_x = height, my_y = mass)

starwars |> 
  filter(mass < 500) |> 
  make_scatterplot_with_vars(height, birth_year)

cars |> 
  make_scatterplot_with_vars(speed, dist)

Abstracting the function

In the tidyverse context we have to take into account data masking by indirectly embracing data variables

• Environment variables show up in the Environment tab
• Data variables are columns in a data frame

map()

Apply a function to each element of a vector or list

Can also do this in Base-R with apply(), sapply(), mapply(), lapply()

nest()

starwars |> 
  select(name, gender, height, mass) |> 
  nest(my_data_by_gender = -gender) 

# A tibble: 3 × 2
  gender    my_data_by_gender
  <chr>     <list>           
1 masculine <tibble [66 × 3]>
2 feminine  <tibble [17 × 3]>
3 <NA>      <tibble [4 × 3]>